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Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance

Author

Listed:
  • Chiweta Emmanuel Abunike

    (School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UK
    Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Abia State, Nigeria)

  • Ogbonnaya Inya Okoro

    (Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Abia State, Nigeria)

  • Sumeet S. Aphale

    (School of Engineering, University of Aberdeen, Aberdeen AB24 3UE, UK)

Abstract

In this paper, a thorough framework for multiobjective design optimization of switched reluctance motor (SRM) is proposed. Selection of stator and rotor pole embrace coefficients is an essential step in the SRM design process since it influences torque output and torque ripple in SRM. The problem of determining optimal pole embrace is formulated as a multi-objective optimization problem with the objective of optimizing average torque, efficiency and torque ripple, and response surface models were obtained based on the genetic aggregation method. The results obtained by genetic aggregation response surface (GARS) and the non-dominated genetic algorithm (NSGA-II) were validated with the finite element method (FEM) model of the initial SRM. The optimized model displayed better efficiency profile over a wide speed range. The initial and optimized models recorded maximum efficiencies of 85% and 94.05%, respectively, at 2000 rpm. The efficiency values of 93.97–94.05% were achieved for the three pareto optimal candidates. The findings indicate the viability of the suggested strategy and support the use of GARS and NSGA-II as useful methods for addressing SRM key challenges.

Suggested Citation

  • Chiweta Emmanuel Abunike & Ogbonnaya Inya Okoro & Sumeet S. Aphale, 2022. "Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance," Energies, MDPI, vol. 15(16), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:6086-:d:894788
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    References listed on IDEAS

    as
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